View/Open

Download Record

Author

Date

Advisor

Metadata

Abstract

This thesis investigates detection and classification issues when dealing with seismic signals and represents a first step in the direction of automated detection and classification of mine-like signals obtained using a seismic approach. A computationally cheap detection scheme that utilizes a combination of a simple combination of a short- term energy and zero-crossing detector is implemented and tested on five different classes of targets, resulting in a 100% detection rate for all non-natural targets and 33% detection rate of mine sized rock buried in sand. Three feature extraction methods are evaluated for their possible use in a Gaussian Mixture Model classifier: higher order moments, pole extraction from impulse response modeling using the Steiglitz-McBride iteration, and Radial Basis Function Modeling of data. These methods demonstrate promising results for use in a classifier. However, only a very limited number of data trials per class was available in this work, and the proposed set-up needs to be further validated with additional data.

Related items

Detection of small targets in the presence of noise and sea clutter interference presents a formidable task in a radar system design. Conventional radar detection schemes, such as spectral discrimination and noncoherent ...

Attack aircraft on interdiction or deep support missions are faced
with the problem of detecting their targets by visual means. Much has
been written about the general theory of computing detection probabilities
associated ...

This study analyses the trend for initial detection times using both passive and active sonar during submarine-on-submarine operations. Specifically, it simulates a nuclear powered submarine (SSN) searching for a diesel ...